Approximating TSP on metrics with bounded global growth

• Authors: T-H. Hubert Chan;Anupam Gupta
• Venue: Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
• Year: 2008

The Traveling Salesman Problem (TSP) is a canonical NP-complete problem which is known to be MAX-SNP hard even on (high-dimensional) Euclidean metrics [39]. In order to circumvent this hardness, researchers have been developing approximation schemes for low-dimensional metrics [4, 38] (under different notions of dimension). However, a feature of most current notions of metric dimension is that they are "local": the definitions require every local neighborhood to be well-behaved. In this paper, we consider the case when the metric is less restricted: it has a few "dense" regions, but is "well-behaved on the average"? To this end, we define a global notion of dimension which we call the correlation dimension (denoted by dimC), which generalizes the popular notion of doubling dimension. In fact, the class of metrics with dimC = O(1) not only contains all doubling metrics, but also contains some metrics containing uniform submetrics of size √n. We first show, using a somewhat "local" argument, that one can solve TSP on these metrics in time 2O(√n); we then take advantage of the global nature of TSP (and the global nature of our definition) to give a (1 + ε)-approximation algorithm that runs in sub-exponential time: i.e., in 2O(nδε-4dimC)-time for every constant 0